Time perception is the capacity to sense the passing of time, but in most living creatures it also involves memorizing how much time passed, and eventually acting when it reaches a specific amount. The later is referred as interval timing. This capacity allows animals to detect temporally repeating events in their environment, avoid them if necessary, or exploit them if beneficial(Saigusa et al., 2008). While the research in animals has focused on interval timing (Connor, 1985; Durstewitz, 2003), research in artificial life has limited itself to time perception (Maniadakis et al., 2014; Trianni, 2008). Indeed, alife models rely on the intrinsic temporal properties of neural networks to encode the passing of time and, therefore, cannot estimate how much time passed since the onset of a stimulus. Our work attempts to make one step closer to interval timing by designing an agent which must learn the duration of a stimulus, but also replay it later on.
|Title of host publication||Proceedings of the Artificial Life Conference 2016|
|Editors||Carlos Gershenson, Tom Froese, Jesus M. Siqueiros, Wendy Aguilar, Eduardo J. Izquierdo, Hiroki Sayama|
|Publication status||Published - 2016|
- Neural Network
- Time Perception
- Evolutionary Robotics